MARL-Ped: A multi-agent reinforcement learning based framework to simulate pedestrian groups

Pedestrian simulation is complex because there are different levels of behavior modeling. At the lowest level, local interactions between agents occur; at the middle level, strategic and tactical behaviors appear like overtakings or route choices; and at the highest level path-planning is necessary. The agent-based pedestrian simulators either focus on a specific level (mainly in the lower one) or define strategies like the layered architectures to independently manage the different behavioral levels. In our Multi-Agent Reinforcement-Learning-based Pedestrian simulation framework (MARL-Ped) the situation is addressed as a whole. Each embodied agent uses a model-free Reinforcement Learning (RL) algorithm to learn autonomously to navigate in the virtual environment. The main goal of this work is to demonstrate empirically that MARL-Ped generates learned behaviors adapted to the level required by the pedestrian scenario. Three different experiments, described in the pedestrian modeling literature, are presented to test our approach: (i) election of the shortest path vs. quickest path; (ii) a crossing between two groups of pedestrians walking in opposite directions inside a narrow corridor; (iii) two agents that move in opposite directions inside a maze. The results show that MARL-Ped solves the different problems, learning individual behaviors with characteristics of pedestrians (local control that produces adequate fundamental diagrams, route-choice capability, emergence of collective behaviors and path-planning). Besides, we compared our model with that of Helbing's social forces, a well-known model of pedestrians, showing similarities between the pedestrian dynamics generated by both approaches. These results demonstrate empirically that MARL-Ped generates variate plausible behaviors, producing human-like macroscopic pedestrian flow.